Heuristic Search

Theory and Applications


  • Stefan Edelkamp, Senior Researcher and Lecturer at University of Bremen
  • Stefan Schroedl, Senior Scientist at Yahoo!, Inc.

Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed.

Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us.

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Researchers, professors, and graduate students


Book information

  • Published: June 2011
  • ISBN: 978-0-12-372512-7


"Heuristic Search is a very solid monograph and textbook on (not only heuristic) search. In its presentation it is always more formal than colloquial, it is precise and well structured. Due to its spiral approach it motivates reading it in its entirety."--Zentralblatt MATH 2012-1238-68150
"The authors have done an outstanding job putting together this book on artificial intelligence (AI) heuristic state space search. It comprehensively covers the subject from its basics to the most recent work and is a great introduction for beginners in this field."--BCS.org
"Heuristic search lies at the core of Artificial Intelligence and it provides the foundations for many different approaches in problem solving. This book provides a comprehensive yet deep description of the main algorithms in the field along with a very complete discussion of their main applications. Very well-written, it embellishes every algorithm with pseudo-code and technical studies of their theoretical performance."--Carlos Linares López, Universidad Carlos III de Madrid
"This is an introduction to artificial intelligence heuristic state space search. Authors Edelkamp (U. of Bremen, Germany) and Schrödl (a research scientist at Yahoo! Labs) seek to strike a balance between search algorithms and their theoretical analysis, on the one hand, and their efficient implementation and application to important real-world problems on the other, while covering the field comprehensively from well-known basic results to recent work in the state of the art. Prior knowledge of artificial intelligence is not assumed, but basic knowledge of algorithms, data structures, and calculus is expected. Proofs are included for formal rigor and to introduce proof techniques to the reader. They have organized the material into five sections: heuristic search primer, heuristic search under memory constraints, heuristic search under time constraints, heuristic search variants, and applications."--SciTech Book News
"This almost encyclopedic text is suitable for advanced courses in artificial intelligence and as a text and reference for developers, practitioners, students, and researchers in artificial intelligence, robotics, computational biology, and the decision sciences. The exposition is comparable to texts for a graduate-level or advanced undergraduate course in computer science, and prior exposure or coursework in advanced algorithms, computability, or artificial intelligence would help a great deal in understanding the material. Algorithms are described in pseudocode, accompanied by diagrams and narrative explanations in the text. The vast size of the ‚Äėsearch algorithms‚Äô subject domain and the variety of applications of search mean that much information--especially pertaining to applications of search algorithms--had to be left out; however, an extensive (though still limited) bibliography is included for follow-up by the reader. Exercises are provided for each chapter, except the five chapters on applications, and bibliographic notes accompany all chapters."--Computing Reviews

Table of Contents

I Heuristic Search Primer

1 Introduction
1.1 Notational and Mathematical Background
1.2 Search
1.3 Success Stories
1.4 State Space Problems
1.5 Problem Graph Representations
1.6 Heuristics
1.7 Examples of Search Problems
1.8 General State Space Descriptions
1.9 Summary
1.10 Exercises
1.11 Bibliographic Notes

2 Basic Search Algorithms
2.1 Uninformed Graph Search Algorithms
2.2 Informed Optimal Search
2.3 General Weights
2.4 Summary
2.5 Exercises
2.6 Bibliographic Notes

3 Dictionary Data Structures
3.1 Priority Queues
3.2 Hash Tables
3.3 Subset Dictionaries
3.4 String Dictionaries
3.5 Summary
3.6 Exercises
3.7 Bibliographic Notes

4 Automatically Created Heuristics
4.1 Abstraction Transformations
4.2 Valtorta’s Theorem
4.3 Hierarchical A
4.4 Pattern Databases
4.5 Customized Pattern Databases
4.6 Summary
4.7 Exercises
4.8 Bibliographic Notes

II Heuristic Search under Memory Constraints
5 Linear-Space Search
5.1 Logarithmic Space Algorithms
5.2 Exploring the Search Tree
5.3 Branch-and-Bound
5.4 Iterative Deepening Search
5.5 Iterative Deepening A
5.6 Prediction of IDA Search
5.7 Refined Threshold Determination
5.8 Recursive Best-First Search
5.9 Summary
5.10 Exercises
5.11 Bibliographic Notes

6 Memory Restricted Search
6.1 Linear Variants using Additional Memory
6.2 Non-Admissible Search
6.3 Reduction of the Closed List
6.4 Reduction of the Open List
6.5 Summary
6.6 Exercises
6.7 Bibliographic Notes

7 Symbolic Search
7.1 Boolean Encodings for Set of States
7.2 Binary Decision Diagrams
7.3 Computing the Image for a State Set
7.4 Symbolic Blind Search
7.5 Limits and Possibilities of BDDs
7.6 Symbolic Heuristic Search
7.7 Refinements
7.8 Symbolic Algorithms for Explicit Graphs
7.9 Summary
7.10 Exercises
7.11 Bibliographic Notes

8 External Search
8.1 Virtual Memory Management
8.2 Fault Tolerance
8.3 Model of Computation
8.4 Basic Primitives
8.5 External Explicit Graph Search
8.6 External Implicit Graph Search
8.7 Refinements
8.8 External Value Iteration
8.9 Flash Memory
8.10 Summary
8.11 Exercises
8.12 Bibliographic Notes

III Heuristic Search under Time Constraints
9 Distributed Search
9.1 Parallel Processing
9.2 Parallel Depth-First Search
9.3 Parallel Best-first Search Algorithms
9.4 Parallel External Search
9.5 Parallel Search on the GPU
9.6 Bidirectional Search
9.7 Summary
9.8 Exercises
9.9 Bibliographic Notes

10 State Space Pruning
10.1 Admissible State Space Pruning
10.3 Summary
10.4 Exercises
10.5 Bibliographic Notes

11 Real-Time Search by Sven Koenig
11.1 LRTA
11.2 LRTA with Lookahead One
11.3 Analysis of the Execution Cost of LRTA
11.4 Features of LRTA
11.5 Additional Variants of LRTA
11.6 Examples for How to Use Real-Time Search
11.7 Summary
11.8 Exercises
11.9 Bibliography

IV Heuristic Search Variants
12 Adversary Search
12.1 Two-Player Games
12.2 Multi-Player Games
12.3 General Game Playing
12.4 AND/OR Graph Search
12.5 Summary
12.6 Bibliographic Notes

13 Constraint Search
13.1 Constraint Satisfaction
13.2 Consistency
13.3 Search Strategies
13.4 NP-hard Problem Solving
13.5 Temporal Constraint Networks
13.6 Path Constraints
13.7 Soft and Preference Constraints
13.8 Constraint Optimization
13.9 Summary
13.10 Exercises
13.11 Bibliographic Notes

14 Selective Search
14.1 From State Space Search to Minimization
14.2 Hill-Climbing Search
14.3 Simulated Annealing
14.4 Tabu Search
14.5 Evolutionary Algorithms
14.6 Approximate Search
14.7 Randomized Search
14.8 Ant Algorithms
14.9 Lagrange Multipliers
14.10 No-Free Lunch
14.12 Exercises
14.13 Bibliographic Notes .

V Heurstic Search Applications

15 Action Planning
15.1 Optimal Planning
15.2 Suboptimal Planning
15.3 Bibliographic Notes

16 Automated System Verification
16.1 Model Checking .
16.2 Communication Protocols
16.3 Program Model Checking
16.4 Analyzing Petri Nets
16.5 Exploring Real-Time Systems
16.6 Analyzing Graph Transition Systems
16.7 Anomalies in Knowledge Bases
16.8 Diagnosis
16.9 Automated Theorem Proving
16.10 Bibliographic Notes

17 Vehicle Navigation
17.2 Routing Algorithms
17.3 Cutting Corners
17.4 Bibliographic Notes

18 Computational Biology
18.1 Biological Pathway
18.2 Mulitple Sequence Allignment
18.3 Bibliographic Notes
19 Robotics by Sven Koenig
19.1 Search Spaces
19.2 Search with Incomplete Information
19.3 Fundamental Robot-Navigation Tasks
19.4 Planning
19.5 Bibliographic Notes